54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
"""基于当前大屏和会话历史流式回答问题"""
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.output_parsers import StrOutputParser
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from langgraph.config import get_stream_writer
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from app.clients.llm import get_llm
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from app.processor.base import BaseNode
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from app.processor.state import QueryState
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from app.prompt.qa import QA_SYSTEM_PROMPT
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class QANode(BaseNode):
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name = "qa_node"
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async def process(self, state: QueryState) -> QueryState:
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updated_state: QueryState = state.copy()
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try:
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# 图执行上下文中通过 custom 流逐段推送回答，独立调用节点时没有写入器
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writer = get_stream_writer()
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except RuntimeError:
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writer = None
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parser = StrOutputParser()
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parts: list[str] = []
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async for chunk in get_llm().astream(
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[
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SystemMessage(content=QA_SYSTEM_PROMPT),
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HumanMessage(
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content=state["input_context"].model_dump_json(exclude_unset=True)
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),
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]
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):
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# 工具调用等无文本片段会得到空串
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if delta := parser.invoke(chunk):
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parts.append(delta)
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if writer is not None:
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writer(
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{
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"type": "answer_delta",
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"message_id": state.get("message_id"),
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"delta": delta,
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}
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)
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answer = "".join(parts).strip()
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# 没有有效文本时报错，避免将空回答标记为处理完成
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if not answer:
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raise ValueError("问答模型未返回文本回答")
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updated_state["answer"] = answer
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updated_state["status"] = "completed"
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return updated_state
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